Journal ArticleDOI
Efficient Sequential Designs with Binary Data
TLDR
In this article, a class of sequential designs for estimating the percentiles of a quantal response curve is proposed, which can be viewed as a natural analog of the Robbins-Monro procedure in the case of binary data.Abstract:
A class of sequential designs for estimating the percentiles of a quantal response curve is proposed. Its updating rule is based on an efficient summary of all of the data available via a parametric model. The logit-MLE version of the proposed designs can be viewed as a natural analog of the Robbins—Monro procedure in the case of binary data. It is shown to be asymptotically consistent, optimal, and nonparametric via its connection with the latter procedure. For certain choices of initial designs, the proposed method performs very well in a simulation study for sample sizes up to 35. A nonparametric sequential design, via the Spearman—Karber estimator, for estimating the median is also proposed.read more
Citations
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Journal ArticleDOI
Bayesian Experimental Design: A Review
TL;DR: This paper reviews the literature on Bayesian experimental design, both for linear and nonlinear models, and presents a uniied view of the topic by putting experimental design in a decision theoretic framework.
Response Surface Methodology.
TL;DR: A survey of the various stages in the development of response surface methodology RSM is given in this article, which includes a review of basic experimental designs for fitting linear response surface models, in addition to a description of methods for the determination of optimum operating conditions.
Journal ArticleDOI
Response surface methodology
TL;DR: A survey of the various stages in the development of response surface methodology RSM is provided, organized in three parts that describe the evolution of RSM since its introduction in the early 1950s.
Journal ArticleDOI
Experimental design
TL;DR: Experimental design is reviewed here for broad classes of data collection and analysis problems, including: fractioning techniques based on orthogonal arrays, Latin hypercube designs and their variants for computer experimentation, efficient design for data mining and machine learning applications, and sequential design for active learning.
Journal ArticleDOI
Constrained Monte Carlo Maximum Likelihood for Dependent Data
TL;DR: In this paper, a Markov chain Monte Carlo method is used to approximate the whole likelihood function in autologistic models and other exponential family models for dependent data, and the parameter value (if any) maximizing this function approximates the MLE.
References
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Journal ArticleDOI
A Stochastic Approximation Method
Herbert Robbins,Sutton Monro +1 more
TL;DR: In this article, a method for making successive experiments at levels x1, x2, ··· in such a way that xn will tend to θ in probability is presented.
Book
analysis of binary data
David Cox,E. J. Snell +1 more
TL;DR: Binary response variables special logistical analyses some complications some related approaches more complex responses.
Journal ArticleDOI
A Method for Obtaining and Analyzing Sensitivity Data
TL;DR: This paper provides an alternative technique based on a special system for obtaining sensitivity of dosage-mortality data that has some advantages when observations must be taken on individuals rather than groups of individuals, and it may be preferred in certain other situations.